Abstract
The paper presents a hybrid approach to social network analysis for obtaining information on suspicious user profiles. The offered approach is based on integration of statistical techniques, data mining and visual analysis. The advantage of the proposed approach is that it needs limited kinds of social network data (“likes” in groups and links between users) which is often in open access. The results of experiments confirming the applicability of the proposed approach are outlined.
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Aiello, L.M., et al.: People are strange when you’re a stranger: impact and influence of bots on social networks. In: International AAAI Conference on Weblogs and Social Media (2012)
Cook, D.M.: Birds of a feather deceive together: the chicanery of multiplied metadata. J. Inf. Warfare 13(4), 85–96 (2014)
Davis, C.A., et al.: Botornot: a system to evaluate social bots. In: Proceedings of the 25th International Conference Companion on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 273–274 (2016)
Duh, A., Rupnik, S.M., Korošak, D.: Collective behavior of social bots is encoded in their temporal twitter activity. Big Data 6(2), 113–123 (2018)
Ferrara, E., et al.: The rise of social bots. Commun. ACM 59(7), 96–104 (2016)
Gavra, D.P., Dekalov, V.V.: Communicative capitaland communicative exploitation in digital society. In: 2018 IEEE Communication Strategies in Digital Society Workshop (ComSDS), pp. 22–26. IEEE (2018)
Gorodetsky, V., Tushkanova, O.: Data-driven semantic concept analysis for user profile learning in 3G recommender systems. In: 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Singapore, pp. 92–97 (2015). https://doi.org/10.1109/wi-iat.2015.80
Kotenko, I., Chechulin, A., Komashinsky, D.: Categorisation of web pages for protection against inappropriate content in the internet. Int. J. Internet Protoc. Technol. (IJIPT) 10(1), 61–71 (2017)
Nougayrede, N.: In this age of propaganda, we must defend ourselves. Here’s how, The Guardian (31/01/18) (2018). Accessed 28 Mar 2018.https://www.theguardian.com/commentisfree/2018/jan/31/propaganda-defend-russia-technology
Pronoza, A., Vitkova, L., Chechulin, A., Kotenko, I.: Visual analysis of information dissemination channels in social network for protection against inappropriate content. In: 3rd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2018. Sochi, Russian Federation, 17–21 September 2018, Advances in Intelligent Systems and Computing, vol. 875, pp. 95–105 (2019)
Puri, R.: Bots & botnet: an overview. SANS Inst. 3, 58 (2003)
Ratkiewicz, J., et al.: Detecting and tracking political abuse in social media. In: Fifth International AAAI Conference on Weblogs and Social Media (2011)
Satya, B.P.R., et al.: Uncovering fake likers in online social networks. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 2365–2370. ACM (2016)
Shu, K., et al.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newsl. 19(1), 22–36 (2017)
Thawonmas, R., Kashifuji, Y., Chen, K.-T.: Detection of MMORPG bots based on behavior analysis. In: Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (ACE 2008), pp. 91–94. ACM, New York (2008). https://doi.org/10.1145/1501750.1501770
Varol, O., et al.: Online human-bot interactions: detection, estimation, and characterization. In: Eleventh International AAAI Conference on Web and Social Media (2017)
Varol, O., et al.: Early detection of promoted campaigns on social media. EPJ Data Sci. 6(1), 13 (2017)
Wardle, C., Derakhshan, H.: Information disorder: towards an interdisciplinary framework for research and policy-making. Council of Europe (2017). https://firstdraftnews.com/resource/coe-report/
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This research was supported by the Russian Science Foundation under grant number 18-71-10094 in SPIIRAS.
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Vitkova, L., Kotenko, I., Kolomeets, M., Tushkanova, O., Chechulin, A. (2020). Hybrid Approach for Bots Detection in Social Networks Based on Topological, Textual and Statistical Features. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-50097-9_42
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DOI: https://doi.org/10.1007/978-3-030-50097-9_42
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